ABSTRACT
Spatial distribution rules and risk factors for syphilis were studied in Jiangsu province, People's Republic of China during 2005 and 2009. Trend surface analysis, spatial autocorrelation analysis and spatio-temporal clustering were applied with the incidence rates of the various counties in the province to determine spatial distribution rules and risk factors. Syphilis was found to be most severe in the southern region of the province where many counties could be shown to be hotspots with positive autocorrelation. Clusters were detected in the south-western region of Jiangsu with the county-level city of Yixing as the centre. Temperature, distance from railways and highways, and the normalised difference vegetation index were determined as supporting variables with regard to the transmission of the disease by both univariate and multivariate spatial correlation analyses. Interventions, including health education and awareness campaigns, should be strengthened throughout the province targeting the south-western areas, especially the clusters and hotspots detected in order to improve the situation.
Subject(s)
Sex Workers/statistics & numerical data , Syphilis/epidemiology , Transients and Migrants/statistics & numerical data , Transportation/statistics & numerical data , China/epidemiology , Geographic Information Systems , Humans , Incidence , Poisson Distribution , Population Density , Prevalence , Railroads/statistics & numerical data , Regression Analysis , Risk Factors , Seasons , Space-Time Clustering , Syphilis/prevention & control , Syphilis/transmission , Transportation/methodsABSTRACT
Risk maps for the geographical distribution of human immunodeficiency virus (HIV) and the acquired immune deficiency syndrome (AIDS) are needed for the direction of HIV prevention interventions. Our study, based on county-level data on the numbers of HIV/AIDS patients in the Yunnan province, People's Republic of China, applied trend surface analysis and spatial autocorrelation analysis to demonstrate the geographical distribution of HIV-positive patients in the province. The case load of HIV was found to be most severe in the central-west region of the province. While Kunming county was shown to be negatively correlated with its surrounding counties, many high-burden counties are surrounded by other counties with similar case numbers. We conclude that intervention efforts in Yunnan province should concentrate on the western and northeast regions, targeting the hotspots of infection.
